A simple optimization can improve the performance of single feature polymorphism detection by Affymetrix expression arrays

<p>Abstract</p> <p>Background</p> <p>High-density oligonucleotide arrays are effective tools for genotyping numerous loci simultaneously. In small genome species (genome size: < ~300 Mb), whole-genome DNA hybridization to expression arrays has been used for various applications. In large genome species, transcript hybridization to expression arrays has been used for genotyping. Although rice is a fully sequenced model plant of medium genome size (~400 Mb), there are a few examples of the use of rice oligonucleotide array as a genotyping tool.</p> <p>Results</p> <p>We compared the single feature polymorphism (SFP) detection performance of whole-genome and transcript hybridizations using the Affymetrix GeneChip^®Rice Genome Array, using the rice cultivars with full genome sequence, <it>japonica </it>cultivar Nipponbare and <it>indica </it>cultivar 93-11. Both genomes were surveyed for all probe target sequences. Only completely matched 25-mer single copy probes of the Nipponbare genome were extracted, and SFPs between them and 93-11 sequences were predicted. We investigated optimum conditions for SFP detection in both whole genome and transcript hybridization using differences between perfect match and mismatch probe intensities of non-polymorphic targets, assuming that these differences are representative of those between mismatch and perfect targets. Several statistical methods of SFP detection by whole-genome hybridization were compared under the optimized conditions. Causes of false positives and negatives in SFP detection in both types of hybridization were investigated.</p> <p>Conclusions</p> <p>The optimizations allowed a more than 20% increase in true SFP detection in whole-genome hybridization and a large improvement of SFP detection performance in transcript hybridization. Significance analysis of the microarray for log-transformed raw intensities of PM probes gave the best performance in whole genome hybridization, and 22,936 true SFPs were detected with 23.58% false positives by whole genome hybridization. For transcript hybridization, stable SFP detection was achieved for highly expressed genes, and about 3,500 SFPs were detected at a high sensitivity (> 50%) in both shoot and young panicle transcripts. High SFP detection performances of both genome and transcript hybridizations indicated that microarrays of a complex genome (e.g., of <it>Oryza sativa</it>) can be effectively utilized for whole genome genotyping to conduct mutant mapping and analysis of quantitative traits such as gene expression levels.</p

Oxaliplatin for Metastatic Colon Cancer in a Patient with Renal Failure

The efficacy, safety, pharmacokinetics, and dialysability of oxaliplatin were assessed in a hemodialysis patient with recurrent cecal cancer

Common Peak Approach Using Mass Spectrometry Data Sets for Predicting the Effects of Anticancer Drugs on Breast Cancer

We propose a method for biomarker discovery from mass spectrometry data, improving the common peak approach developed by Fushiki et al. (BMC Bioinformatics, 7:358, 2006). The common peak method is a simple way to select the sensible peaks that are shared with many subjects among all detected peaks by combining a standard spectrum alignment and kernel density estimates. The key idea of our proposed method is to apply the common peak approach to each class label separately. Hence, the proposed method gains more informative peaks for predicting class labels, while minor peaks associated with specific subjects are deleted correctly. We used a SELDI-TOF MS data set from laser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using an anticancer drug on breast cancer patients. The AdaBoost algorithm is adopted for pattern recognition, based on the set of candidate peaks selected by the proposed method. The analysis gives good performance in the sense of test errors for classifying the class labels for a given feature vector of selected peak values

The Type Ic Hypernova SN 2002ap

Photometric and spectroscopic data of the energetic Type Ic supernova (SN) 2002ap are presented, and the properties of the SN are investigated through models of its spectral evolution and its light curve. The SN is spectroscopically similar to the "hypernova" SN 1997ef. However, its kinetic energy [$\sim (4-10) \times 10^{51}$ erg] and the mass ejected (2.5-5 $M_{\odot}$) are smaller, resulting in a faster-evolving light curve. The SN synthesized $\sim 0.07 M_{\odot}$ of $^{56}$Ni, and its peak luminosity was similar to that of normal SNe. Brightness alone should not be used to define a hypernova, whose defining character, namely very broad spectral features, is the result of a high kinetic energy. The likely main-sequence mass of the progenitor star was 20-25 $M_{\odot}$, which is also lower than that of both hypernovae SNe 1997ef and 1998bw. SN 2002ap appears to lie at the low-energy and low-mass end of the hypernova sequence as it is known so far. Observations of the nebular spectrum, which is expected to dominate by summer 2002, are necessary to confirm these values.Comment: 10 pages, 4 figures, accepted for publication in ApJL, 30 April 2002 (minor changes to match the accepted version, with figures being colored

Boosting method for local learning in statistical classification

The main objective is to study boosting methods in statistical classification. Several ensemble learning methods including boosting have attracted many researchers’ interests in the last decade. In particular, it has been reported that the boosting methods perform well in many practical classification problems. The boosting algorithm constructs an accurate classifier by combining several base classifiers, which are often at most slightly more accurate than random guess. While many researchers have studied the boosting methods, their success has still some mysterious aspects. More intensive theoretical studies are required to clarify such mysteries.　We describe a survey on several ensemble learning methods. We set up the statistical classification problem and make some notations to develop discussion from learning theories. Some theoretical preliminaries for analyzing the performance of classification　methods are also overviewed. Then, we survey some existing ensemble learning methods. In particular, we review theoretical properties of boosting methods, which have been clarified by several researchers.　The application of AdaBoost with decision stumps to shark bycatch data from the Eastern Pacific Ocean tuna purse-seine fishery is described. Generalized additive models (GAMs) are one of the most widely-used tools for analyzing fisheries data. It is well known that AdaBoost is closely connected to logistic GAMs when appropriate base classifiers are used. We compared results of AdaBoost to those obtained from GAMs. Compared to the logistic GAM, the prediction performance of AdaBoost was more stable, even with correlated features. Standard deviations of the test error were often considerably smaller for AdaBoost than for the logistic GAM. In addition, AdaBoost score plots, graphical displays of the contribution of each feature to the discriminant function, were also more stable than score plots of the logistic GAM, particularly in regions of sparse data. AsymBoost, a variant of AdaBoost developed for binary classification of a skewed response variable, was also shown to be effective at reducing the false negative ratio without substantially increasing the overall test error. Boosting with decision stumps, however, may not capture complicated structures in general since decision stumps are considerably simple classifiers. Use of more complicated base classifiers possibly improves the approximation ability of boosting. However, several literatures have pointed out that the use of complicated base classifiers may increase the generalization error of boosting. In addition, it is difficult to find what types of base classifiers are appropriate to each problem without any prior knowledge.　To overcome these difficulties, we propose a new method, the local boosting, that is a localized version of boosting method based on the idea similar to but not the same as the local likelihood. Application of the local likelihood may improve the approximation ability considerably but also increases the computational cost, which makes the algorithm infeasible. The local boosting, however, includes a simple device for computational feasibility. We show that the local boosting has the Bayes risk consistency in the framework of PAC learning. It is seen that the estimation error increases compared to the ordinary boosting with simple base classifiers when we use the ordinary boosting with more complicated base classifiers or when we use the local boosting. However, the increase caused by the local boosting is not large. When same base classifiers are used, the local boosting attains the Bayes risk consistency in wider situations than the ordinary boosting by controlling the trade-off between estimation error and approximation error. Several simulations confirm the theoretical results and the effectiveness of the local boosting over the ordinary boosting in both binary and multiclass classifications